AI Software Development Engineer - Neuromorphic Computing

Intel Corporation•Santa Clara, CA

9d•$170,500 - $240,710•Hybrid

About The Position

Intel's Neuromorphic Computing Lab is advancing neuromorphic architectures, algorithms, and software for physical AI systems such as robots and intelligent edge devices. The AI Software Development Engineer will turn new neuromorphic hardware capabilities into high-performance AI software, build specialized kernels for current and next-generation hardware, and help shape programming abstractions for Intel's next-generation neuromorphic architecture. The role spans the hardware-software lifecycle: modeling hardware features, implementing kernels, optimizing end-to-end applications for Loihi-based systems, and demonstrating measurable gains on AI workloads. The engineer will work with hardware architects, compiler engineers, AI researchers, and other engineering teams, using performance and power models to inform hardware decisions and validating results through simulators, emulators, and hardware. As part of Intel's CTO Office, the role contributes to an incubation effort focused on bringing neuromorphic technology innovations to market.

Requirements

PhD with no prior professional experience, master's degree with at least 2 years of relevant experience, or bachelor's degree with at least 4 years of relevant experience in Computer Science, Electrical Engineering, Computer Engineering, Applied Mathematics, Physics, or a related technical field.
At least 4 years of experience developing, debugging, and delivering maintainable software in Python and either C or C++, including performance-critical or systems-level code.
At least 2 years of experience implementing and optimizing numerical, machine-learning, or high-performance computing kernels using parallel programming and either an accelerator programming model such as CUDA, SYCL, or OpenCL, or a domain-specific language such as Triton.
At least 2 years of experience developing, training, or evaluating AI algorithms and machine-learning models using a framework such as PyTorch, JAX, or TensorFlow.
At least 2 years of experience establishing numerical correctness and measuring performance using reference implementations, automated testing, benchmarking, profiling, or performance-regression infrastructure.

Nice To Haves

Experience developing or optimizing workloads based on convolutional networks, transformers, generative AI, or other models relevant to edge and physical AI systems.
Experience with compiler technologies or domain-specific language development, such as MLIR, LLVM, TVM, Triton, or related toolchains.
Experience with hardware performance modeling, architecture simulators, performance, power, and area analysis, or hardware-software co-design.
Advanced experience with optimization techniques including kernel fusion, tiling, vectorization, quantization, sparsity, layout optimization, or double buffering.
Experience developing software for spatial, dataflow, neuromorphic, or other emerging AI accelerator architectures.
Experience contributing to collaborative or open-source software projects using code review, continuous integration, documentation, and testing practices.

Responsibilities

Design and implement specialized AI kernels for neuromorphic hardware using custom DSLs and accelerator programming models such as CUDA and SYCL, and help shape programming abstractions for Intel's next-generation neuromorphic architecture.
Develop and optimize operations for convolutional networks, transformers, and generative AI workloads using techniques such as tiling, fusion, vectorization, parallelization, layout transformation, buffering, sparsity, and quantization.
Develop performance methodologies and improve kernel behavior in compute utilization, latency, throughput, memory bandwidth, data movement, synchronization, and scaling, translating architectural insights into measurable workload gains.
Build reference models, numerical validation tools, benchmarks, and analytical or simulation-based performance, power, and area models to guide hardware-software co-design.
Reconcile discrepancies across models, simulators, emulators, and hardware.
Collaborate with hardware, compiler, runtime, and application engineers to deliver maintainable code, tests, documentation, benchmarks, and performance-regression infrastructure.
Contribute to training, porting, and optimization of end-to-end applications for Loihi-based systems.